Top PDF The Osgood condition for stochastic partial differential equations

The Osgood condition for stochastic partial differential equations

The Osgood condition for stochastic partial differential equations

We end this introduction with some remarks concerning local existence of solutions when the equations are defined on the whole space. D. Khoshnevisan pointed to us that since for any fixed t > 0, the last term of (1.6) grows like √ log x as x goes to infinity, the solution to (1.6) might blow up instantaneously. That is, any solution of (1.6) can blow up for any t > 0 so that there is no local solution. In the deterministic setting, similar phenomenon arises; see for instance [16] where the exponential reaction-diffusion is studied. Proving such non- existence results is beyond the scope of this paper where the main concern is non-existence of global solution. The above result for instance makes no claim about the existence of a local solution.
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Integral Inequality and Exponential Stability for Neutral Stochastic Partial Differential Equations with Delays

Integral Inequality and Exponential Stability for Neutral Stochastic Partial Differential Equations with Delays

However, comparing with stochastic partial differential equations with delays, there are only a few results about neutral stochastic partial differential equations. Precisely, Liu 11 considered a linear neutral stochastic differential equations with constant delays and some stability properties of the mild solutions in a similar way as Datko 25 in the deterministic case. Caraballo et al., in 3, have studied the almost sure exponential stability and ultimate boundedness of the solutions to a class of neutral stochastic semilinear partial delay differential equations; Mahmudov, in 14, has discussed the existence and uniqueness for mild solution of neutral stochastic differential equations by constructing a new iterative scheme under the non-Lipschitz conditions.
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Stochastic Runge-Kutta method for stochastic delay differential equations

Stochastic Runge-Kutta method for stochastic delay differential equations

solving SDDEs in various fields. It can also be shown in this research, the SRK methods are easy to implement compare to the approximation methods obtained from the truncating stochastic Taylor expansion. In this way the computation of high–order partial derivatives can be avoided. Moreover, the generalization of convergence proof when the drift and diffusion functions are Taylor expansion is hoped can facilitate mathematicians to explore this area more widely.

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Boundary value problems for stochastic differential equations

Boundary value problems for stochastic differential equations

BOUNDARY VALUE PROBLEHS FOR STOCHASTIC DIFFERENTIAL EQUATIONS Thesis by Thomas 1 Tilliam HacDm rell In Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy California Institu[.]

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Finite Difference Approximation for Linear Stochastic Partial Differential Equations with Method of Lines

Finite Difference Approximation for Linear Stochastic Partial Differential Equations with Method of Lines

The second application would be to provide a numerical scheme for approximating the solution of a forward backward stochastic differential equation (FBSDE) with ran- dom coefficients. When the coefficients are deterministic the FBSDE (i.e. Markovian case) can be solved by using the Four Step Scheme of Ma, Protter and Yong [8], which requires that a deterministic PDE be solved. When the coefficients of the FBSDE are random, it has been shown in Ma and Yong [9, 10, 11] that the Four Step Scheme requires the solution of parabolic and elliptic SPDEs for the fi nite and in fi nite cases, respectively.
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Computer algebra resolves a multitude of microscale interactions to model stochastic partial differential equations

Computer algebra resolves a multitude of microscale interactions to model stochastic partial differential equations

reproduce and check many of the details described by Roberts [2]. We consider a small spatial domain, representing a finite element, and ap- ply stochastic centre manifold techniques to derive a one degree of freedom model for the dynamics in the element. The approach auto- matically parametrises the microscale structures induced by spatially varying stochastic noise within the element. The crucial aspect of this work is that we explore how many noise processes may interact in nonlinear dynamics.

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Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations

Adaptive Methods Exploring Intrinsic Sparse Structures of Stochastic Partial Differential Equations

Direct tracking of KL expansions. In the popular numerical methods for SPDEs, such as MC, qMC, gPC, and gSC, additional post-processing steps are necessary to get the KL expansions of stochastic solutions. Without any additional post-processing steps, DyBO methods explore the inherent low-dimensional structures and give directly the stochastic solutions in bi-orthogonal form that essentially tracks the KL expansion of the stochastic solutions. The ability to preserve bi- orthogonal forms in DyBO methods has been proved rigorously and verified numerically in various challenging problems, such as stochastic Burgers equations, and stochastic flows in 2D unit square. Reduced-order models and computation reductions. An important benefit associated with DyBO formulations is the significant savings of computational costs both in memory consumptions and computational times. Detailed complexity analysis has been conducted for DyBO-gPC methods for certain classes of time-dependent SPDEs and verified numerically in Chapter 4.2. In practice, we have observed speedup up to 200 times compared to gPC methods in 2D stochastic flow problems.
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Stochastic differential equations in a scale of Hilbert spaces

Stochastic differential equations in a scale of Hilbert spaces

(β − α) −1/2 (and make similar assumption about the diffusion coefficient B), see Condition 2.1 given in the next section, and prove the existence and uniqueness of finite time solutions of the corresponding Cauchy problem. Observe that the singularity allowed here is weaker than in the deterministic case (cf. (1.2)), which is related to the specifics of the Ito integral estimates. As in the deterministic case, the solution will live in the scale X α , α ∈ A . For simplicity, we assume

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Backward stochastic partial differential equations driven by infinite dimensional martingales and applications

Backward stochastic partial differential equations driven by infinite dimensional martingales and applications

with A(t, ω) being a predictable linear operator on H that belongs to L(V ; V ′ ), where (V, H, V ′ ) is a rigged Hilbert space or the called Gelfand’s triple. Now assuming that A(t, ω) is coercive for a.a. (t, ω) ∈ [0, T ] × Ω and F satisfies a similar condition to the ones given earlier, we shall show that this BSPDE admits a unique solution (Y, Z, N ) of predictable processes taking values in V × L 2 (H) × M 2,c (H) and that Y is a continuous semimartingale. This space

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On the Effects of Different Interpretations of Stochastic Differential Equations

On the Effects of Different Interpretations of Stochastic Differential Equations

DOI: 10.4236/am.2019.1011063 878 Applied Mathematics Section 2). 3) Stratonovich’s interpretation has sounder mathematical and phys- ical bases: a) the ordinary rules of calculus are preserved; b) it is invariant with respect to a time reversal and guarantees the condition of detailed balance [29]; c) in the case of a dynamical system it respects the law of the energy conservation [32]. 4) The kinetic interpretation is the only that agrees with the law of the thermodynamic [11] [27]. 5) The experiments are in accord with Stratonovich’s interpretation [20] [21]. 6) The FPK equation deriving from Stratonovich’s in- terpretation has one more term in the drift, the so-called spurious drift that does not originate from a physical reason (it is recalled that this term coincides with the Wong-Zakai-Stratonovich corrective term [33] [34], which makes Itô’s solu- tion of the FPK equation equal to Stratonovich’s one). In [16] it is supposed that the spurious drift is caused by the infinitely fast fluctuations of the Gaussian white noise. No attempt is made here to give a meaning to the spurious drift.
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Development, Optimization, and Analysis of Cellular Automaton Algorithms to Solve Stochastic Partial Differential Equations

Development, Optimization, and Analysis of Cellular Automaton Algorithms to Solve Stochastic Partial Differential Equations

Due to the mathematically complex nature of stochastic processes, there are limited cases in which SPDEs can be solved analytically. Moreover, contemporary numerical algorithms are not optimal for deriving an exact form of their solutions either; so, we are compelled to seek an accurate and efficient method to better approximate these equations. A newly developing method to approximate the solution of SPDEs involves the use of cellular automata. The utilization of cellular automata allows one to represent the diffusion processes associated with SPDEs in a discretized n-dimensional mesh-based grid, like a dynamical system. The concept of cellular automaton was developed by Neumann and Ulmann in the 1940s. This topic developed throughout the 20th and 21st centuries through the popularization of various models, such as Conway’s Game of Life. In 2002, Wolfram published A New Kind of Science [2] in which he asserts that the use of cellular automaton has a multitude of applications. The work discussed in this paper is a continuation on this foundational work.
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Asymptotic behaviours of stochastic differential delay equations

Asymptotic behaviours of stochastic differential delay equations

Most of the existing results on stochastic stability use a single Lyapunov function, but we shall instead use multiple Lyapunov functions in this paper. We shall establish the sufficient condition, in terms of multiple Lyapunov functions, for the asymptotic behaviours of solutions of stochas- tic differential delay equations. Moreover, from them follow many effective criteria on stochastic asymptotic stability, which enable us to construct the Lyapunov functions much more easily in applications. In particular, the well-known classical theorem on stochastic asymptotic stability is a special case of our more general results. These show clearly the power of our new results. Two examples are also given for illustration.
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Improved bridge constructs for stochastic differential equations

Improved bridge constructs for stochastic differential equations

In this section we describe a novel class of bridge constructs that require no tuning parameters, are simple to implement (even when only a subset of components are observed with Gaussian noise) and can account for nonlinear dynamics driven by the drift. In addition, we discuss the recently pro- posed bridging strategy of Schauer et al. (2016) and describe an implementation method in the case of partial observation with additive Gaussian measurement error.

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Multidimensional stochastic differential equations with distributional drift

Multidimensional stochastic differential equations with distributional drift

other types of transforms to study similar equations. Indeed the transfor- mation introduced by Zvonkin in [27], when the drift is a function, is also stated in the multidimensional case. In a series of papers the first named author and coauthors (see for instance [9]), have efficiently made use of a (multidimensional) Zvonkin type transform for the study of an SDE with measurable not necessarily bounded drift, which however is still a function. Zvonkin transform consisted there to transform a solution X of (2) (which makes sense being a classical SDE) through a solution ϕ : [0, T ] × R d → R d of a PDE which is close to the associated Kolmogorov equation (3) with some suitable final condition. The resulting process Y with Y t = ϕ(t, X t )
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Malliavin calculus for backward stochastic differential equations and stochastic differential equations driven by fractional Brownian motion and numerical schemes

Malliavin calculus for backward stochastic differential equations and stochastic differential equations driven by fractional Brownian motion and numerical schemes

determined by a quasi-linear partial differential equation of parabolic type. Recently, Bouchard and Touzi [4] propose a Monte-Carlo approach which may be more suitable for high-dimensional problems. Again in this forward-backward setting, if the genera- tor f has a quadratic growth in Z, a numerical approximation is developed by Imkeller and Dos Reis [20] in which a truncation procedure is applied.

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On stochastic differential equations and a generalised Burgers equation

On stochastic differential equations and a generalised Burgers equation

chatacterised by a single representative agent maximising expected utility for wealth at terminal date H, they demonstrate, by using binormial tree argument in the discrete time setting and Taylor expansion, that a necessary condition for economic equilibria is that the ratio α follows the following Burgers equation

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Discontinuous Quantum Stochastic Differential Equations and The Associated Kurzweil Equations

Discontinuous Quantum Stochastic Differential Equations and The Associated Kurzweil Equations

This definition of the class Car (Ã × [a, b], μ) concerning the map g(x, s)(𝜂, 𝜉) is closely related to the class C (Ã × [a, b], W) in [1]. Indeed, if μ is the Lebesgue measure W (t) = t on [a, b], then they are the same except that (2.3) and (2.4) here are required to hold everywhere instead of μ - almost everywhere. In the definition of the class Car (Ã × [a, b], μ), (2.5) from Definition 2.1 of the class C (Ã × [a, b], μ, W) is replaced by (2.10). The condition expressed by (2.5) requires that the continuity from (2.10) has a given modulus W. It is obvious that C (Ã × [a, b], μ, W) Car (Ã × [a, b], μ).
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Exponential stability of mild solutions to impulsive stochastic neutral partial differential equations with memory

Exponential stability of mild solutions to impulsive stochastic neutral partial differential equations with memory

motivated by [–], we study the exponential stability of impulsive stochastic neutral partial differential equations with memory by establishing a new integral inequality. The results obtained here generalize the main results from [, , ] to cover a class of more general impulsive stochastic neutral systems.

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A white noise approach to stochastic partial differential equations driven by the fractional Lévy noise

A white noise approach to stochastic partial differential equations driven by the fractional Lévy noise

In recent years, fractional Lévy processes are getting popular since they are more flexible in modeling the distributions of noises than fractional Brownian motions (FBMs). More pre- cisely, they can capture large jumps and model high variability in the real systems appear- ing in finance, telecommunications, and so on. Meanwhile, they can also capture the long memory effect in a similar way as FBMs do (see [1, 6–9, 12, 13], etc.). Currently, more and more researchers have been attracted to the studies of fractional Lévy processes, stochastic calculus for fractional Lévy processes, and stochastic differential equations driven by these processes. In [14], the authors defined a stochastic integral for a class of deterministic inte- grands with respect to real-valued fractional Lévy processes. In [13], we defined a stochas- tic integral for a class of real deterministic functions and deterministic operator-valued processes with respect to fractional Lévy processes on Gel’fand triple. In [1], by using S- transform the authors investigated the Skorokhod integral for fractional Lévy processes whose underlying Lévy processes have finite moments of any order by avoiding Malliavin calculus and white noise analysis. In [11], a white noise theory for fractional Lévy process was developed by considering it as a generalized functional of the sample path of Lévy process and solving several kinds of stochastic ordinary differential equations driven by fractional Lévy noises.
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Accurate stationary densities with partitioned numerical methods for stochastic partial differential equations

Accurate stationary densities with partitioned numerical methods for stochastic partial differential equations

Abstract We consider the numerical solution, by finite differences, of second-order-in-time stochastic par- tial differential equations (SPDEs) in one space dimension. New timestepping methods are introduced by generalising recently-introduced methods for second-order-in-time stochastic differential equations to multi- dimensional systems. These stochastic methods, based on leapfrog and Runge-Kutta methods, are designed to give good approximations to the stationary variances and the correlations in the position and veloc- ity variables. In particular, we introduce the reverse leapfrog method and stochastic Runge-Kutta Leapfrog methods, analyse their performance applied to linear SPDEs and perform numerical experiments to examine their accuracy applied to a type of nonlinear SPDE.
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